One of the major challenges which any cellular network operator faces is to ensure that the network is operating to its maximum efficiency. As a result, cellular network optimization is a major feature of many modern cellular networks.
In order to provide the best possible performance to the cellular network subscribers, the network is periodically optimized so that its resources can be more effectively utilized within the core network and/or the Radio Access Network (“RAN”).
Typically, network optimization is affected by manually modifying network parameters in the Radio and Core Networks based on information that relates to network performance. Such information is retrieved periodically and analyzed by the Operations and Support System (OSS) to derive Key Performance Indicators (KPIs) therefrom. The state of the art KPIs include typical system level (e.g. related to user or cell throughputs) and link level (e.g. various transmission error rates) metrics.
Traditional optimization methods are slow, operate with a high degree of granularity, and have a long turnaround time. Optimization of a communication network using presently available tools basically entails changing one static parameter setup to another followed by several iterations of a cumbersome verification stage.
In order to support rapidly changing network needs, it would be highly beneficial to have a fully integrated automated load balancing application with a built in feedback mechanism, thereby freeing the operators from their tedious roles of manual optimization to software applications and focus on defining network policies, performance goals and network plans.
Several solutions have been proposed in the art for analyzing a wired/wireless communication network to optimize its performance.
US 2005064820 describes continuously collecting data from all elements constituting the communication network and analyzing the data to find an element of which performance and/or efficiency deteriorates.
US 2004085909 discloses scheduling transmissions in a wireless communication system using historical information and usage patterns of remote users in the system. Usage patterns for users within a system are stored and analyzed to optimize transmissions and resources in the system.
US 2010029282 describes collecting various wireless performance metrics by respective network access points as an aggregate measure of the wireless network performance. Aggregated data can be utilized to generate a performance model for the network and for individual access points. Changes to the data are updated to the model to provide a steady-state characterization of network performance. Wireless resources are generated for respective access points in a manner that optimizes wireless performance. Additionally, resource assignments can be updated at various intervals to re-optimize for existing wireless conditions, whether event driven or based on performance metrics. Accordingly, a robust and dynamic optimization is provided for wireless network resource provisioning that can accommodate heterogeneous access point networks in a changing topology.
US 20060068712 relates to a method of correlating probed data captured from various interfaces to create a combined picture at a call level. Thus, the method described allows real time distributed analysis and troubleshooting of the data on the interfaces of N radio network controllers from a single location.
US 20080139197 discloses providing a probe application by a network server for downloading by a mobile device. The probe application monitors a level of performance for various use applications provided by the network for the mobile device, and reports the monitored level of performance for at least one of the applications to the network server. The network server collates the performance data from the plurality of communication devices and provides resource allocation instructions to the mobile in order to optimize a level of performance for the use applications for the communication device.
Our co-pending application U.S. Ser. No. 13/680,779 filed Nov. 19, 2012 describes a computing platform for optimizing operation of a cellular network by: (a) probing for information exchanged between a mobile access network and a core network; (b) retrieving statistical KPIs generated by a plurality of network elements; (c) predicting a trend characterizing future performance of cells; and (d) triggering changes in the operation of the cellular network based on the predicted trend.
However, there is still a need for a solution that provides further optimization capabilities for operating cellular networks, such that can take into account traffic load effects by using a pre-selected cluster of cells and using parameter settings derived from such considerations, thereby enabling further optimization of the performance of a network under near real time conditions.